{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "772080ef-db96-4b42-8e68-eaa7a5bced76"
    }
   },
   "source": [
    "# 程序说明\n",
    "- cv2读取的img是[height, width, channels]\n",
    "- jupyter按`ESC`后按`h`获取快捷键说明\n",
    "- `shift`+ `l` 行标\n",
    "- 其中前两个参数为人体框左上角点的坐标值，后两个参数为人体框右下角点的坐标值。(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "135341a6-e204-4e4f-a43d-a62caa956590"
    }
   },
   "outputs": [],
   "source": [
    "import pickle as pk\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "e6a07c7b-0004-4f0b-87a1-a706f47c183c"
    }
   },
   "outputs": [],
   "source": [
    "anno = pd.read_json('../demo_data/keypoint_train_annotations_20170909.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "6c9b40af-4c3c-4d29-be68-942bd8a5859c"
    }
   },
   "outputs": [],
   "source": [
    "anno.drop(['url'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "0264c75b-5b44-4adb-8bda-5a8f7727a656"
    }
   },
   "outputs": [],
   "source": [
    "import cv2 \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 先对原始的.json文件处理,使其能在长和宽维度上扩大30%,从而获得新的框的坐标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "b1870046-3b04-4ce8-94e2-60889dbd8614"
    }
   },
   "outputs": [],
   "source": [
    "img_wh = []\n",
    "for img in anno.image_id:\n",
    "    path = '/media/bnrc2/_backup/ai/ai_challenger_keypoint_train_20170902/keypoint_train_images_20170902/{}.jpg'.format(img)\n",
    "    img_np = cv2.imread(path)\n",
    "    img_wh.append(np.shape(img_np)[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "ac89acd5-70b4-438a-b341-ce5fee24bcbe"
    }
   },
   "outputs": [],
   "source": [
    "anno['image_height_width'] = img_wh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "nbpresent": {
     "id": "e5b3f316-407a-42b1-946b-87620a1456fe"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>human_annotations</th>\n",
       "      <th>image_id</th>\n",
       "      <th>keypoint_annotations</th>\n",
       "      <th>image_height_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'human2': [374, 238, 572, 743], 'human1': [13...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'human3': [580, 49, 941, 593], 'human2': [395...</td>\n",
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       "      <td>(356, 530)</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>{'human1': [653, 116, 779, 651]}</td>\n",
       "      <td>62151d25e5caa2bccb1b571a5a6cf371369028b4</td>\n",
       "      <td>{'human1': [683, 249, 1, 676, 175, 1, 731, 140...</td>\n",
       "      <td>(666, 1000)</td>\n",
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       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>{'human3': [608, 102, 886, 441], 'human2': [21...</td>\n",
       "      <td>7aeec8e64afd04c23c4988a647a851a3e7d52ccb</td>\n",
       "      <td>{'human3': [670, 290, 1, 683, 395, 1, 803, 381...</td>\n",
       "      <td>(626, 940)</td>\n",
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       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>{'human2': [218, 133, 460, 742], 'human1': [44...</td>\n",
       "      <td>2bd9db0a2599ba6ca8c3351c471a29e24c36af3e</td>\n",
       "      <td>{'human2': [263, 310, 1, 266, 435, 1, 317, 523...</td>\n",
       "      <td>(750, 500)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>{'human1': [82, 8, 512, 733]}</td>\n",
       "      <td>d78fe2735a101dd45d8e931a80bd8f6a460dd587</td>\n",
       "      <td>{'human1': [140, 316, 1, 142, 491, 1, 138, 631...</td>\n",
       "      <td>(735, 550)</td>\n",
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       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>{'human2': [0, 43, 345, 923], 'human1': [140, ...</td>\n",
       "      <td>7df555633d2e860814a047dbc5d3a5bc0b22fa25</td>\n",
       "      <td>{'human2': [27, 169, 1, 0, 0, 3, 0, 0, 3, 166,...</td>\n",
       "      <td>(1000, 666)</td>\n",
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       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>{'human1': [216, 75, 772, 635]}</td>\n",
       "      <td>9a7f971d79a4e00158e20b1f462236bb4dfba344</td>\n",
       "      <td>{'human1': [416, 202, 1, 354, 280, 1, 287, 336...</td>\n",
       "      <td>(665, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>{'human2': [284, 4, 819, 666], 'human1': [0, 2...</td>\n",
       "      <td>040dc1b0e086f0415adb6e8389e3c20b505c38c0</td>\n",
       "      <td>{'human2': [437, 373, 1, 410, 510, 1, 417, 629...</td>\n",
       "      <td>(666, 1000)</td>\n",
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       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>{'human6': [832, 309, 945, 598], 'human5': [69...</td>\n",
       "      <td>522bbcea7b6243c83ae467397467a64853c5764a</td>\n",
       "      <td>{'human6': [860, 369, 1, 845, 404, 2, 838, 434...</td>\n",
       "      <td>(666, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>{'human3': [54, 24, 435, 661], 'human2': [260,...</td>\n",
       "      <td>a1f254a8dc1cbfbede5aab5b49a3b677f29e77a6</td>\n",
       "      <td>{'human3': [259, 143, 2, 170, 171, 1, 116, 224...</td>\n",
       "      <td>(693, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>{'human1': [44, 107, 447, 890]}</td>\n",
       "      <td>0bbc67ad0ee0c94f553ce7757a67b743c79a80b1</td>\n",
       "      <td>{'human1': [244, 268, 1, 165, 342, 1, 223, 385...</td>\n",
       "      <td>(940, 668)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>{'human4': [480, 0, 680, 330], 'human3': [597,...</td>\n",
       "      <td>8b8c7e42e2c03b6e79a384d9dfab84dbf772d191</td>\n",
       "      <td>{'human4': [0, 0, 3, 0, 0, 3, 0, 0, 3, 0, 0, 3...</td>\n",
       "      <td>(646, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>{'human1': [200, 231, 490, 889]}</td>\n",
       "      <td>9920d24cb34e1c2947bf446853d9f5a5354ed0bf</td>\n",
       "      <td>{'human1': [283, 374, 1, 325, 460, 1, 357, 540...</td>\n",
       "      <td>(1000, 711)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>{'human2': [104, 111, 353, 939], 'human1': [29...</td>\n",
       "      <td>96d42665d10bc6bbe4908a891b4e553eb9676661</td>\n",
       "      <td>{'human2': [174, 276, 1, 155, 394, 1, 129, 484...</td>\n",
       "      <td>(941, 600)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>{'human3': [648, 136, 928, 702], 'human2': [33...</td>\n",
       "      <td>a0a57eb5135c7db48688a500708265e7636908ef</td>\n",
       "      <td>{'human3': [767, 234, 1, 717, 301, 1, 678, 380...</td>\n",
       "      <td>(795, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>{'human2': [101, 120, 192, 482], 'human1': [15...</td>\n",
       "      <td>4f8f57734666ce759f0baa3b6f6068d0b0699b53</td>\n",
       "      <td>{'human2': [133, 195, 2, 128, 259, 2, 119, 296...</td>\n",
       "      <td>(751, 500)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>{'human2': [326, 70, 586, 928], 'human1': [55,...</td>\n",
       "      <td>7e5abdf7dd47a8de196014c9bd4bb7b904ddd444</td>\n",
       "      <td>{'human2': [351, 257, 1, 362, 376, 1, 415, 475...</td>\n",
       "      <td>(939, 626)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>{'human1': [143, 169, 641, 953]}</td>\n",
       "      <td>144182728d1d2edde9dcf8f5324b6c76998add2d</td>\n",
       "      <td>{'human1': [335, 308, 2, 296, 352, 1, 201, 343...</td>\n",
       "      <td>(1000, 665)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>{'human1': [117, 35, 417, 898]}</td>\n",
       "      <td>9352648e31d3f7afc6dea79bb28506808e348465</td>\n",
       "      <td>{'human1': [158, 202, 1, 166, 336, 1, 289, 324...</td>\n",
       "      <td>(960, 640)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>{'human2': [369, 398, 643, 944], 'human1': [12...</td>\n",
       "      <td>4cf280565329eb8dc721c7d2521505596e2d8089</td>\n",
       "      <td>{'human2': [496, 529, 1, 435, 541, 1, 392, 500...</td>\n",
       "      <td>(1000, 769)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>{'human2': [467, 135, 665, 786], 'human1': [19...</td>\n",
       "      <td>45e3848616c5ba3db4eca73f81c6f5c88e1789c6</td>\n",
       "      <td>{'human2': [637, 271, 1, 651, 388, 1, 649, 444...</td>\n",
       "      <td>(1000, 667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>{'human2': [342, 101, 583, 710], 'human1': [70...</td>\n",
       "      <td>7e0ab7c43a0deb97168825aafd5071485731407c</td>\n",
       "      <td>{'human2': [392, 176, 1, 384, 208, 2, 378, 220...</td>\n",
       "      <td>(718, 691)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>{'human2': [498, 142, 561, 341], 'human1': [71...</td>\n",
       "      <td>1d6d88db2d2930942eb83f1818129942dafdf6d8</td>\n",
       "      <td>{'human2': [516, 187, 1, 512, 220, 1, 510, 241...</td>\n",
       "      <td>(421, 750)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>{'human1': [124, 25, 514, 631]}</td>\n",
       "      <td>bb0fc3527876071ff1732cfdddf13492b92fd2e1</td>\n",
       "      <td>{'human1': [197, 177, 1, 199, 305, 1, 314, 366...</td>\n",
       "      <td>(634, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>{'human1': [169, 116, 636, 954]}</td>\n",
       "      <td>bb1733674134cad32e802318891ac80f098a8d3f</td>\n",
       "      <td>{'human1': [341, 295, 1, 441, 314, 1, 549, 259...</td>\n",
       "      <td>(1000, 750)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>{'human1': [165, 57, 377, 817]}</td>\n",
       "      <td>bc7b7a0819a0b64727a5cfdc84eabb665516e93e</td>\n",
       "      <td>{'human1': [216, 210, 1, 211, 345, 1, 285, 414...</td>\n",
       "      <td>(818, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>{'human2': [200, 475, 516, 900], 'human1': [10...</td>\n",
       "      <td>ad366355eafe1cf943c2e41e2e0a8d23135fd7d9</td>\n",
       "      <td>{'human2': [457, 617, 1, 466, 556, 1, 444, 515...</td>\n",
       "      <td>(900, 600)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>{'human1': [118, 99, 418, 909]}</td>\n",
       "      <td>282b302a0d5d133e212f03f9ada610b12859941b</td>\n",
       "      <td>{'human1': [215, 271, 1, 145, 380, 1, 221, 398...</td>\n",
       "      <td>(959, 603)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>{'human3': [119, 107, 299, 612], 'human2': [25...</td>\n",
       "      <td>0ade7caa8014689f193e7bbbe47711ad413f4dc2</td>\n",
       "      <td>{'human3': [215, 216, 1, 187, 250, 1, 147, 289...</td>\n",
       "      <td>(677, 900)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>{'human2': [709, 113, 907, 630], 'human1': [42...</td>\n",
       "      <td>e6adb9d437268678c3daa1487c113cfd2c1f383f</td>\n",
       "      <td>{'human2': [849, 224, 1, 873, 293, 1, 885, 366...</td>\n",
       "      <td>(665, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>{'human1': [202, 12, 693, 643]}</td>\n",
       "      <td>c38410f42ea787dc34c66cc459889707ff426803</td>\n",
       "      <td>{'human1': [260, 389, 1, 316, 629, 1, 504, 599...</td>\n",
       "      <td>(644, 930)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>{'human1': [290, 118, 537, 663]}</td>\n",
       "      <td>e3949a4a32ecb7f3089b9a807d2b6620edba91d4</td>\n",
       "      <td>{'human1': [336, 298, 1, 324, 425, 1, 342, 538...</td>\n",
       "      <td>(666, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'human8': [464, 130, 565, 417], 'human7': [52...</td>\n",
       "      <td>094ade15553543ed44a2020d5a19db22062d98a8</td>\n",
       "      <td>{'human8': [513, 203, 2, 525, 244, 2, 512, 279...</td>\n",
       "      <td>(454, 615)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>{'human1': [67, 105, 551, 743]}</td>\n",
       "      <td>b978c1ccc143e004a11a5d5438a5322f40dca554</td>\n",
       "      <td>{'human1': [236, 167, 2, 274, 149, 1, 248, 211...</td>\n",
       "      <td>(800, 591)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>{'human1': [166, 54, 442, 823]}</td>\n",
       "      <td>5408b0fe4dd0383e622136fe51b352fc808ced0f</td>\n",
       "      <td>{'human1': [231, 204, 1, 229, 347, 2, 197, 447...</td>\n",
       "      <td>(843, 564)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>{'human2': [521, 252, 767, 746], 'human1': [18...</td>\n",
       "      <td>75493c9c4d8d3130590dd234e212349747f74ba8</td>\n",
       "      <td>{'human2': [583, 384, 1, 557, 480, 1, 549, 439...</td>\n",
       "      <td>(809, 940)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>{'human2': [186, 125, 315, 561], 'human1': [66...</td>\n",
       "      <td>357bda3796c5372151c993e2456fa50712012ea8</td>\n",
       "      <td>{'human2': [216, 222, 1, 222, 293, 2, 202, 283...</td>\n",
       "      <td>(600, 400)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>{'human3': [564, 93, 780, 509], 'human2': [1, ...</td>\n",
       "      <td>011e32d88ddd78cf844719c22bee78f0c4d9fd4e</td>\n",
       "      <td>{'human3': [754, 220, 2, 772, 318, 2, 758, 391...</td>\n",
       "      <td>(900, 875)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>{'human1': [142, 63, 366, 810]}</td>\n",
       "      <td>768696b4fe1ecc31e5ec8de30acdf6bb94c2f282</td>\n",
       "      <td>{'human1': [212, 222, 2, 189, 319, 2, 168, 412...</td>\n",
       "      <td>(811, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>{'human1': [179, 1, 510, 687]}</td>\n",
       "      <td>fd9b76dee6f1d003eda67592b1bfb5fa256d4f81</td>\n",
       "      <td>{'human1': [245, 220, 1, 212, 346, 1, 230, 425...</td>\n",
       "      <td>(687, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>{'human2': [237, 77, 470, 782], 'human1': [65,...</td>\n",
       "      <td>864d09b2f91c4d802af30a7c3f08304d5b09a98b</td>\n",
       "      <td>{'human2': [264, 238, 2, 278, 311, 2, 292, 277...</td>\n",
       "      <td>(825, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>{'human1': [84, 98, 647, 875]}</td>\n",
       "      <td>7282ff93c7ffccc31561de917c47f4b1b3593ef8</td>\n",
       "      <td>{'human1': [388, 260, 1, 464, 334, 1, 557, 286...</td>\n",
       "      <td>(939, 741)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>{'human2': [94, 181, 448, 639], 'human1': [477...</td>\n",
       "      <td>781827a1c05024b4520b14f880d89e1c764c32ca</td>\n",
       "      <td>{'human2': [319, 271, 1, 264, 260, 1, 202, 216...</td>\n",
       "      <td>(707, 948)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>{'human1': [16, 4, 684, 778]}</td>\n",
       "      <td>6c893f6f5eb02c8abf2fe696334373564fe4d988</td>\n",
       "      <td>{'human1': [80, 326, 1, 102, 484, 1, 114, 646,...</td>\n",
       "      <td>(800, 694)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>{'human2': [421, 136, 739, 791], 'human1': [14...</td>\n",
       "      <td>6f3c83aa9fc82f12f6dc0dd8bfc4092df34a4dca</td>\n",
       "      <td>{'human2': [539, 282, 1, 504, 368, 1, 465, 450...</td>\n",
       "      <td>(948, 793)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>{'human1': [113, 97, 546, 926]}</td>\n",
       "      <td>3dd8d04858029eecebbbcf1d0297bece1161b174</td>\n",
       "      <td>{'human1': [167, 286, 1, 231, 412, 1, 349, 478...</td>\n",
       "      <td>(1000, 666)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>{'human1': [453, 88, 807, 562]}</td>\n",
       "      <td>276f3b6e41f3f4a973bb8a4482a41fe97ab386db</td>\n",
       "      <td>{'human1': [510, 241, 1, 492, 345, 1, 562, 340...</td>\n",
       "      <td>(606, 900)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>{'human1': [158, 20, 396, 813]}</td>\n",
       "      <td>818cf5d67ed235a0b206703cf143c72fea87525a</td>\n",
       "      <td>{'human1': [212, 172, 2, 192, 298, 1, 221, 206...</td>\n",
       "      <td>(825, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>{'human2': [216, 42, 533, 587], 'human1': [232...</td>\n",
       "      <td>c5aa021fab5092a0465744f107020503f6e0878c</td>\n",
       "      <td>{'human2': [360, 176, 1, 352, 272, 1, 396, 309...</td>\n",
       "      <td>(657, 940)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>{'human2': [196, 183, 564, 802], 'human1': [16...</td>\n",
       "      <td>477ac3ce26bdcd029b37aba8c00dca0b359a6d8d</td>\n",
       "      <td>{'human2': [320, 297, 1, 271, 373, 2, 235, 422...</td>\n",
       "      <td>(1000, 742)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>{'human3': [0, 111, 352, 666], 'human2': [335,...</td>\n",
       "      <td>404596910e5dc7fc1ea37f5a05fb28c387580002</td>\n",
       "      <td>{'human3': [35, 265, 1, 0, 0, 3, 25, 329, 1, 1...</td>\n",
       "      <td>(666, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>{'human3': [213, 36, 541, 803], 'human2': [66,...</td>\n",
       "      <td>30ecff9a3207c3b885534d347f2bb00add0dc8a9</td>\n",
       "      <td>{'human3': [342, 223, 1, 346, 360, 1, 256, 405...</td>\n",
       "      <td>(805, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>{'human1': [70, 97, 897, 498]}</td>\n",
       "      <td>3a1376d13a11f00e2f0023e911af6a27faaff2be</td>\n",
       "      <td>{'human1': [141, 307, 1, 97, 406, 1, 127, 441,...</td>\n",
       "      <td>(633, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>{'human2': [308, 42, 784, 525], 'human1': [91,...</td>\n",
       "      <td>9f2f8a6038be76040fbeaca4e8c64fb1f61d02f6</td>\n",
       "      <td>{'human2': [645, 183, 1, 581, 325, 1, 481, 284...</td>\n",
       "      <td>(532, 800)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     human_annotations  \\\n",
       "0    {'human2': [374, 238, 572, 743], 'human1': [13...   \n",
       "1    {'human3': [580, 49, 941, 593], 'human2': [395...   \n",
       "10   {'human2': [264, 67, 390, 355], 'human1': [327...   \n",
       "100                   {'human1': [181, 151, 467, 886]}   \n",
       "101  {'human2': [532, 48, 706, 666], 'human1': [349...   \n",
       "102  {'human4': [501, 245, 585, 497], 'human3': [27...   \n",
       "103  {'human2': [157, 152, 329, 284], 'human1': [12...   \n",
       "104                   {'human1': [653, 116, 779, 651]}   \n",
       "105  {'human3': [608, 102, 886, 441], 'human2': [21...   \n",
       "106  {'human2': [218, 133, 460, 742], 'human1': [44...   \n",
       "107                      {'human1': [82, 8, 512, 733]}   \n",
       "108  {'human2': [0, 43, 345, 923], 'human1': [140, ...   \n",
       "109                    {'human1': [216, 75, 772, 635]}   \n",
       "11   {'human2': [284, 4, 819, 666], 'human1': [0, 2...   \n",
       "110  {'human6': [832, 309, 945, 598], 'human5': [69...   \n",
       "111  {'human3': [54, 24, 435, 661], 'human2': [260,...   \n",
       "112                    {'human1': [44, 107, 447, 890]}   \n",
       "113  {'human4': [480, 0, 680, 330], 'human3': [597,...   \n",
       "114                   {'human1': [200, 231, 490, 889]}   \n",
       "115  {'human2': [104, 111, 353, 939], 'human1': [29...   \n",
       "116  {'human3': [648, 136, 928, 702], 'human2': [33...   \n",
       "117  {'human2': [101, 120, 192, 482], 'human1': [15...   \n",
       "118  {'human2': [326, 70, 586, 928], 'human1': [55,...   \n",
       "119                   {'human1': [143, 169, 641, 953]}   \n",
       "12                     {'human1': [117, 35, 417, 898]}   \n",
       "120  {'human2': [369, 398, 643, 944], 'human1': [12...   \n",
       "121  {'human2': [467, 135, 665, 786], 'human1': [19...   \n",
       "122  {'human2': [342, 101, 583, 710], 'human1': [70...   \n",
       "123  {'human2': [498, 142, 561, 341], 'human1': [71...   \n",
       "124                    {'human1': [124, 25, 514, 631]}   \n",
       "..                                                 ...   \n",
       "72                    {'human1': [169, 116, 636, 954]}   \n",
       "73                     {'human1': [165, 57, 377, 817]}   \n",
       "74   {'human2': [200, 475, 516, 900], 'human1': [10...   \n",
       "75                     {'human1': [118, 99, 418, 909]}   \n",
       "76   {'human3': [119, 107, 299, 612], 'human2': [25...   \n",
       "77   {'human2': [709, 113, 907, 630], 'human1': [42...   \n",
       "78                     {'human1': [202, 12, 693, 643]}   \n",
       "79                    {'human1': [290, 118, 537, 663]}   \n",
       "8    {'human8': [464, 130, 565, 417], 'human7': [52...   \n",
       "80                     {'human1': [67, 105, 551, 743]}   \n",
       "81                     {'human1': [166, 54, 442, 823]}   \n",
       "82   {'human2': [521, 252, 767, 746], 'human1': [18...   \n",
       "83   {'human2': [186, 125, 315, 561], 'human1': [66...   \n",
       "84   {'human3': [564, 93, 780, 509], 'human2': [1, ...   \n",
       "85                     {'human1': [142, 63, 366, 810]}   \n",
       "86                      {'human1': [179, 1, 510, 687]}   \n",
       "87   {'human2': [237, 77, 470, 782], 'human1': [65,...   \n",
       "88                      {'human1': [84, 98, 647, 875]}   \n",
       "89   {'human2': [94, 181, 448, 639], 'human1': [477...   \n",
       "9                        {'human1': [16, 4, 684, 778]}   \n",
       "90   {'human2': [421, 136, 739, 791], 'human1': [14...   \n",
       "91                     {'human1': [113, 97, 546, 926]}   \n",
       "92                     {'human1': [453, 88, 807, 562]}   \n",
       "93                     {'human1': [158, 20, 396, 813]}   \n",
       "94   {'human2': [216, 42, 533, 587], 'human1': [232...   \n",
       "95   {'human2': [196, 183, 564, 802], 'human1': [16...   \n",
       "96   {'human3': [0, 111, 352, 666], 'human2': [335,...   \n",
       "97   {'human3': [213, 36, 541, 803], 'human2': [66,...   \n",
       "98                      {'human1': [70, 97, 897, 498]}   \n",
       "99   {'human2': [308, 42, 784, 525], 'human1': [91,...   \n",
       "\n",
       "                                     image_id  \\\n",
       "0    5f9ab0e1b59cb27de8adf0255a3730536ed87b07   \n",
       "1    8f2b73eda5bc107b58761be170ea669732af57f2   \n",
       "10   44ccfe343f594896c82b1e50438219744ccbab88   \n",
       "100  e2ff8b814590cf92777cb20bf4c788e54754a6fa   \n",
       "101  edad47c66307b56bd24c87982bcb408df0ad91af   \n",
       "102  09a244a6ee20766f226c0f030ffef7a89a9c2f17   \n",
       "103  3af84f31379fcce70dacb65aca44f298b624ab2c   \n",
       "104  62151d25e5caa2bccb1b571a5a6cf371369028b4   \n",
       "105  7aeec8e64afd04c23c4988a647a851a3e7d52ccb   \n",
       "106  2bd9db0a2599ba6ca8c3351c471a29e24c36af3e   \n",
       "107  d78fe2735a101dd45d8e931a80bd8f6a460dd587   \n",
       "108  7df555633d2e860814a047dbc5d3a5bc0b22fa25   \n",
       "109  9a7f971d79a4e00158e20b1f462236bb4dfba344   \n",
       "11   040dc1b0e086f0415adb6e8389e3c20b505c38c0   \n",
       "110  522bbcea7b6243c83ae467397467a64853c5764a   \n",
       "111  a1f254a8dc1cbfbede5aab5b49a3b677f29e77a6   \n",
       "112  0bbc67ad0ee0c94f553ce7757a67b743c79a80b1   \n",
       "113  8b8c7e42e2c03b6e79a384d9dfab84dbf772d191   \n",
       "114  9920d24cb34e1c2947bf446853d9f5a5354ed0bf   \n",
       "115  96d42665d10bc6bbe4908a891b4e553eb9676661   \n",
       "116  a0a57eb5135c7db48688a500708265e7636908ef   \n",
       "117  4f8f57734666ce759f0baa3b6f6068d0b0699b53   \n",
       "118  7e5abdf7dd47a8de196014c9bd4bb7b904ddd444   \n",
       "119  144182728d1d2edde9dcf8f5324b6c76998add2d   \n",
       "12   9352648e31d3f7afc6dea79bb28506808e348465   \n",
       "120  4cf280565329eb8dc721c7d2521505596e2d8089   \n",
       "121  45e3848616c5ba3db4eca73f81c6f5c88e1789c6   \n",
       "122  7e0ab7c43a0deb97168825aafd5071485731407c   \n",
       "123  1d6d88db2d2930942eb83f1818129942dafdf6d8   \n",
       "124  bb0fc3527876071ff1732cfdddf13492b92fd2e1   \n",
       "..                                        ...   \n",
       "72   bb1733674134cad32e802318891ac80f098a8d3f   \n",
       "73   bc7b7a0819a0b64727a5cfdc84eabb665516e93e   \n",
       "74   ad366355eafe1cf943c2e41e2e0a8d23135fd7d9   \n",
       "75   282b302a0d5d133e212f03f9ada610b12859941b   \n",
       "76   0ade7caa8014689f193e7bbbe47711ad413f4dc2   \n",
       "77   e6adb9d437268678c3daa1487c113cfd2c1f383f   \n",
       "78   c38410f42ea787dc34c66cc459889707ff426803   \n",
       "79   e3949a4a32ecb7f3089b9a807d2b6620edba91d4   \n",
       "8    094ade15553543ed44a2020d5a19db22062d98a8   \n",
       "80   b978c1ccc143e004a11a5d5438a5322f40dca554   \n",
       "81   5408b0fe4dd0383e622136fe51b352fc808ced0f   \n",
       "82   75493c9c4d8d3130590dd234e212349747f74ba8   \n",
       "83   357bda3796c5372151c993e2456fa50712012ea8   \n",
       "84   011e32d88ddd78cf844719c22bee78f0c4d9fd4e   \n",
       "85   768696b4fe1ecc31e5ec8de30acdf6bb94c2f282   \n",
       "86   fd9b76dee6f1d003eda67592b1bfb5fa256d4f81   \n",
       "87   864d09b2f91c4d802af30a7c3f08304d5b09a98b   \n",
       "88   7282ff93c7ffccc31561de917c47f4b1b3593ef8   \n",
       "89   781827a1c05024b4520b14f880d89e1c764c32ca   \n",
       "9    6c893f6f5eb02c8abf2fe696334373564fe4d988   \n",
       "90   6f3c83aa9fc82f12f6dc0dd8bfc4092df34a4dca   \n",
       "91   3dd8d04858029eecebbbcf1d0297bece1161b174   \n",
       "92   276f3b6e41f3f4a973bb8a4482a41fe97ab386db   \n",
       "93   818cf5d67ed235a0b206703cf143c72fea87525a   \n",
       "94   c5aa021fab5092a0465744f107020503f6e0878c   \n",
       "95   477ac3ce26bdcd029b37aba8c00dca0b359a6d8d   \n",
       "96   404596910e5dc7fc1ea37f5a05fb28c387580002   \n",
       "97   30ecff9a3207c3b885534d347f2bb00add0dc8a9   \n",
       "98   3a1376d13a11f00e2f0023e911af6a27faaff2be   \n",
       "99   9f2f8a6038be76040fbeaca4e8c64fb1f61d02f6   \n",
       "\n",
       "                                  keypoint_annotations image_height_width  \n",
       "0    {'human2': [430, 370, 1, 400, 444, 1, 474, 451...        (1000, 749)  \n",
       "1    {'human3': [722, 155, 1, 682, 178, 1, 622, 189...        (690, 1000)  \n",
       "10   {'human2': [354, 115, 1, 366, 148, 1, 374, 176...         (356, 530)  \n",
       "100  {'human1': [257, 303, 1, 201, 402, 1, 230, 503...         (939, 633)  \n",
       "101  {'human2': [616, 183, 2, 602, 291, 2, 561, 222...        (667, 1000)  \n",
       "102  {'human4': [568, 295, 1, 556, 316, 2, 537, 293...         (517, 690)  \n",
       "103  {'human2': [275, 191, 1, 256, 200, 1, 235, 206...         (413, 550)  \n",
       "104  {'human1': [683, 249, 1, 676, 175, 1, 731, 140...        (666, 1000)  \n",
       "105  {'human3': [670, 290, 1, 683, 395, 1, 803, 381...         (626, 940)  \n",
       "106  {'human2': [263, 310, 1, 266, 435, 1, 317, 523...         (750, 500)  \n",
       "107  {'human1': [140, 316, 1, 142, 491, 1, 138, 631...         (735, 550)  \n",
       "108  {'human2': [27, 169, 1, 0, 0, 3, 0, 0, 3, 166,...        (1000, 666)  \n",
       "109  {'human1': [416, 202, 1, 354, 280, 1, 287, 336...        (665, 1000)  \n",
       "11   {'human2': [437, 373, 1, 410, 510, 1, 417, 629...        (666, 1000)  \n",
       "110  {'human6': [860, 369, 1, 845, 404, 2, 838, 434...        (666, 1000)  \n",
       "111  {'human3': [259, 143, 2, 170, 171, 1, 116, 224...         (693, 950)  \n",
       "112  {'human1': [244, 268, 1, 165, 342, 1, 223, 385...         (940, 668)  \n",
       "113  {'human4': [0, 0, 3, 0, 0, 3, 0, 0, 3, 0, 0, 3...         (646, 950)  \n",
       "114  {'human1': [283, 374, 1, 325, 460, 1, 357, 540...        (1000, 711)  \n",
       "115  {'human2': [174, 276, 1, 155, 394, 1, 129, 484...         (941, 600)  \n",
       "116  {'human3': [767, 234, 1, 717, 301, 1, 678, 380...         (795, 950)  \n",
       "117  {'human2': [133, 195, 2, 128, 259, 2, 119, 296...         (751, 500)  \n",
       "118  {'human2': [351, 257, 1, 362, 376, 1, 415, 475...         (939, 626)  \n",
       "119  {'human1': [335, 308, 2, 296, 352, 1, 201, 343...        (1000, 665)  \n",
       "12   {'human1': [158, 202, 1, 166, 336, 1, 289, 324...         (960, 640)  \n",
       "120  {'human2': [496, 529, 1, 435, 541, 1, 392, 500...        (1000, 769)  \n",
       "121  {'human2': [637, 271, 1, 651, 388, 1, 649, 444...        (1000, 667)  \n",
       "122  {'human2': [392, 176, 1, 384, 208, 2, 378, 220...         (718, 691)  \n",
       "123  {'human2': [516, 187, 1, 512, 220, 1, 510, 241...         (421, 750)  \n",
       "124  {'human1': [197, 177, 1, 199, 305, 1, 314, 366...         (634, 950)  \n",
       "..                                                 ...                ...  \n",
       "72   {'human1': [341, 295, 1, 441, 314, 1, 549, 259...        (1000, 750)  \n",
       "73   {'human1': [216, 210, 1, 211, 345, 1, 285, 414...         (818, 550)  \n",
       "74   {'human2': [457, 617, 1, 466, 556, 1, 444, 515...         (900, 600)  \n",
       "75   {'human1': [215, 271, 1, 145, 380, 1, 221, 398...         (959, 603)  \n",
       "76   {'human3': [215, 216, 1, 187, 250, 1, 147, 289...         (677, 900)  \n",
       "77   {'human2': [849, 224, 1, 873, 293, 1, 885, 366...        (665, 1000)  \n",
       "78   {'human1': [260, 389, 1, 316, 629, 1, 504, 599...         (644, 930)  \n",
       "79   {'human1': [336, 298, 1, 324, 425, 1, 342, 538...        (666, 1000)  \n",
       "8    {'human8': [513, 203, 2, 525, 244, 2, 512, 279...         (454, 615)  \n",
       "80   {'human1': [236, 167, 2, 274, 149, 1, 248, 211...         (800, 591)  \n",
       "81   {'human1': [231, 204, 1, 229, 347, 2, 197, 447...         (843, 564)  \n",
       "82   {'human2': [583, 384, 1, 557, 480, 1, 549, 439...         (809, 940)  \n",
       "83   {'human2': [216, 222, 1, 222, 293, 2, 202, 283...         (600, 400)  \n",
       "84   {'human3': [754, 220, 2, 772, 318, 2, 758, 391...         (900, 875)  \n",
       "85   {'human1': [212, 222, 2, 189, 319, 2, 168, 412...         (811, 550)  \n",
       "86   {'human1': [245, 220, 1, 212, 346, 1, 230, 425...         (687, 950)  \n",
       "87   {'human2': [264, 238, 2, 278, 311, 2, 292, 277...         (825, 550)  \n",
       "88   {'human1': [388, 260, 1, 464, 334, 1, 557, 286...         (939, 741)  \n",
       "89   {'human2': [319, 271, 1, 264, 260, 1, 202, 216...         (707, 948)  \n",
       "9    {'human1': [80, 326, 1, 102, 484, 1, 114, 646,...         (800, 694)  \n",
       "90   {'human2': [539, 282, 1, 504, 368, 1, 465, 450...         (948, 793)  \n",
       "91   {'human1': [167, 286, 1, 231, 412, 1, 349, 478...        (1000, 666)  \n",
       "92   {'human1': [510, 241, 1, 492, 345, 1, 562, 340...         (606, 900)  \n",
       "93   {'human1': [212, 172, 2, 192, 298, 1, 221, 206...         (825, 550)  \n",
       "94   {'human2': [360, 176, 1, 352, 272, 1, 396, 309...         (657, 940)  \n",
       "95   {'human2': [320, 297, 1, 271, 373, 2, 235, 422...        (1000, 742)  \n",
       "96   {'human3': [35, 265, 1, 0, 0, 3, 25, 329, 1, 1...        (666, 1000)  \n",
       "97   {'human3': [342, 223, 1, 346, 360, 1, 256, 405...         (805, 550)  \n",
       "98   {'human1': [141, 307, 1, 97, 406, 1, 127, 441,...         (633, 950)  \n",
       "99   {'human2': [645, 183, 1, 581, 325, 1, 481, 284...         (532, 800)  \n",
       "\n",
       "[210 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "nbpresent": {
     "id": "ddbd580d-7908-46f9-a2ad-8e2ebab3f1ac"
    }
   },
   "outputs": [],
   "source": [
    "new_coords = []\n",
    "for i in range(anno.shape[0]):\n",
    "    h, w = anno.image_height_width[i]\n",
    "    d = {}\n",
    "    for k, v in anno.human_annotations[i].items():\n",
    "        coords = np.array(v).reshape(-1, 2)\n",
    "        offset = (coords[1] - coords[0]) * 0.15  #沿着长和宽扩大30%\n",
    "        offset = np.concatenate((-offset, offset))\n",
    "        coords = v + offset\n",
    "        coords = coords.astype('int')\n",
    "        coords[np.where(coords<0)] = 0\n",
    "        if coords[2] < w:\n",
    "            coords[2] = w\n",
    "        if coords[3] < h:\n",
    "            coords[3] = h\n",
    "        d[k] = coords\n",
    "    new_coords.append(d)\n",
    "anno.human_annotations = new_coords\n",
    "#         new_coords.append(coords)\n",
    "#         h, w = anno.image_height_width[i]\n",
    "#         hw.append((w, h))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>human_annotations</th>\n",
       "      <th>image_id</th>\n",
       "      <th>keypoint_annotations</th>\n",
       "      <th>image_height_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'human2': [344, 162, 749, 1000], 'human1': [9...</td>\n",
       "      <td>5f9ab0e1b59cb27de8adf0255a3730536ed87b07</td>\n",
       "      <td>{'human2': [430, 370, 1, 400, 444, 1, 474, 451...</td>\n",
       "      <td>(1000, 749)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'human3': [525, 0, 1000, 690], 'human2': [335...</td>\n",
       "      <td>8f2b73eda5bc107b58761be170ea669732af57f2</td>\n",
       "      <td>{'human3': [722, 155, 1, 682, 178, 1, 622, 189...</td>\n",
       "      <td>(690, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>{'human3': [148, 0, 940, 711], 'human2': [0, 0...</td>\n",
       "      <td>44ccfe343f594896c82b1e50438219744ccbab88</td>\n",
       "      <td>{'human2': [354, 115, 1, 366, 148, 1, 374, 176...</td>\n",
       "      <td>(356, 530)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>{'human4': [367, 0, 570, 853], 'human3': [426,...</td>\n",
       "      <td>e2ff8b814590cf92777cb20bf4c788e54754a6fa</td>\n",
       "      <td>{'human1': [257, 303, 1, 201, 402, 1, 230, 503...</td>\n",
       "      <td>(939, 633)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>{'human1': [193, 0, 939, 705]}</td>\n",
       "      <td>edad47c66307b56bd24c87982bcb408df0ad91af</td>\n",
       "      <td>{'human2': [616, 183, 2, 602, 291, 2, 561, 222...</td>\n",
       "      <td>(667, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>{'human1': [56, 18, 743, 1000]}</td>\n",
       "      <td>09a244a6ee20766f226c0f030ffef7a89a9c2f17</td>\n",
       "      <td>{'human4': [568, 295, 1, 556, 316, 2, 537, 293...</td>\n",
       "      <td>(517, 690)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>{'human3': [396, 82, 666, 1000], 'human2': [30...</td>\n",
       "      <td>3af84f31379fcce70dacb65aca44f298b624ab2c</td>\n",
       "      <td>{'human2': [275, 191, 1, 256, 200, 1, 235, 206...</td>\n",
       "      <td>(413, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>{'human2': [0, 91, 641, 710], 'human1': [0, 15...</td>\n",
       "      <td>62151d25e5caa2bccb1b571a5a6cf371369028b4</td>\n",
       "      <td>{'human1': [683, 249, 1, 676, 175, 1, 731, 140...</td>\n",
       "      <td>(666, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>{'human8': [448, 86, 615, 460], 'human7': [513...</td>\n",
       "      <td>7aeec8e64afd04c23c4988a647a851a3e7d52ccb</td>\n",
       "      <td>{'human3': [670, 290, 1, 683, 395, 1, 803, 381...</td>\n",
       "      <td>(626, 940)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>{'human1': [0, 0, 784, 894]}</td>\n",
       "      <td>2bd9db0a2599ba6ca8c3351c471a29e24c36af3e</td>\n",
       "      <td>{'human2': [263, 310, 1, 266, 435, 1, 317, 523...</td>\n",
       "      <td>(750, 500)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>{'human2': [245, 23, 530, 398], 'human1': [306...</td>\n",
       "      <td>d78fe2735a101dd45d8e931a80bd8f6a460dd587</td>\n",
       "      <td>{'human1': [140, 316, 1, 142, 491, 1, 138, 631...</td>\n",
       "      <td>(735, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>{'human2': [203, 0, 1000, 765], 'human1': [0, ...</td>\n",
       "      <td>7df555633d2e860814a047dbc5d3a5bc0b22fa25</td>\n",
       "      <td>{'human2': [27, 169, 1, 0, 0, 3, 0, 0, 3, 166,...</td>\n",
       "      <td>(1000, 666)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>{'human1': [72, 0, 640, 1027]}</td>\n",
       "      <td>9a7f971d79a4e00158e20b1f462236bb4dfba344</td>\n",
       "      <td>{'human1': [416, 202, 1, 354, 280, 1, 287, 336...</td>\n",
       "      <td>(665, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>{'human2': [419, 1, 950, 633], 'human1': [18, ...</td>\n",
       "      <td>040dc1b0e086f0415adb6e8389e3c20b505c38c0</td>\n",
       "      <td>{'human2': [437, 373, 1, 410, 510, 1, 417, 629...</td>\n",
       "      <td>(666, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>{'human1': [102, 0, 564, 992]}</td>\n",
       "      <td>522bbcea7b6243c83ae467397467a64853c5764a</td>\n",
       "      <td>{'human6': [860, 369, 1, 845, 404, 2, 838, 434...</td>\n",
       "      <td>(666, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>{'human1': [0, 0, 630, 1030]}</td>\n",
       "      <td>a1f254a8dc1cbfbede5aab5b49a3b677f29e77a6</td>\n",
       "      <td>{'human3': [259, 143, 2, 170, 171, 1, 116, 224...</td>\n",
       "      <td>(693, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>{'human3': [101, 0, 950, 633], 'human2': [127,...</td>\n",
       "      <td>0bbc67ad0ee0c94f553ce7757a67b743c79a80b1</td>\n",
       "      <td>{'human1': [244, 268, 1, 165, 342, 1, 223, 385...</td>\n",
       "      <td>(940, 668)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>{'human2': [316, 0, 950, 726], 'human1': [26, ...</td>\n",
       "      <td>8b8c7e42e2c03b6e79a384d9dfab84dbf772d191</td>\n",
       "      <td>{'human4': [0, 0, 3, 0, 0, 3, 0, 0, 3, 0, 0, 3...</td>\n",
       "      <td>(646, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>{'human1': [19, 0, 558, 829]}</td>\n",
       "      <td>9920d24cb34e1c2947bf446853d9f5a5354ed0bf</td>\n",
       "      <td>{'human1': [283, 374, 1, 325, 460, 1, 357, 540...</td>\n",
       "      <td>(1000, 711)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>{'human2': [67, 0, 550, 825], 'human1': [83, 0...</td>\n",
       "      <td>96d42665d10bc6bbe4908a891b4e553eb9676661</td>\n",
       "      <td>{'human2': [174, 276, 1, 155, 394, 1, 129, 484...</td>\n",
       "      <td>(941, 600)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>{'human1': [286, 0, 940, 703]}</td>\n",
       "      <td>a0a57eb5135c7db48688a500708265e7636908ef</td>\n",
       "      <td>{'human3': [767, 234, 1, 717, 301, 1, 678, 380...</td>\n",
       "      <td>(795, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>{'human1': [290, 0, 1000, 848]}</td>\n",
       "      <td>4f8f57734666ce759f0baa3b6f6068d0b0699b53</td>\n",
       "      <td>{'human2': [133, 195, 2, 128, 259, 2, 119, 296...</td>\n",
       "      <td>(751, 500)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>{'human1': [104, 0, 600, 997]}</td>\n",
       "      <td>7e5abdf7dd47a8de196014c9bd4bb7b904ddd444</td>\n",
       "      <td>{'human2': [351, 257, 1, 362, 376, 1, 415, 475...</td>\n",
       "      <td>(939, 626)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>{'human3': [0, 118, 799, 1000], 'human2': [0, ...</td>\n",
       "      <td>144182728d1d2edde9dcf8f5324b6c76998add2d</td>\n",
       "      <td>{'human1': [335, 308, 2, 296, 352, 1, 201, 343...</td>\n",
       "      <td>(1000, 665)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>{'human3': [0, 83, 940, 720], 'human2': [83, 0...</td>\n",
       "      <td>9352648e31d3f7afc6dea79bb28506808e348465</td>\n",
       "      <td>{'human1': [158, 202, 1, 166, 336, 1, 289, 324...</td>\n",
       "      <td>(960, 640)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>{'human2': [148, 180, 657, 879], 'human1': [17...</td>\n",
       "      <td>4cf280565329eb8dc721c7d2521505596e2d8089</td>\n",
       "      <td>{'human2': [496, 529, 1, 435, 541, 1, 392, 500...</td>\n",
       "      <td>(1000, 769)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>{'human2': [66, 0, 950, 814], 'human1': [27, 0...</td>\n",
       "      <td>45e3848616c5ba3db4eca73f81c6f5c88e1789c6</td>\n",
       "      <td>{'human2': [637, 271, 1, 651, 388, 1, 649, 444...</td>\n",
       "      <td>(1000, 667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>{'human1': [20, 0, 535, 700]}</td>\n",
       "      <td>7e0ab7c43a0deb97168825aafd5071485731407c</td>\n",
       "      <td>{'human2': [392, 176, 1, 384, 208, 2, 378, 220...</td>\n",
       "      <td>(718, 691)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>{'human1': [5, 0, 626, 1116]}</td>\n",
       "      <td>1d6d88db2d2930942eb83f1818129942dafdf6d8</td>\n",
       "      <td>{'human2': [516, 187, 1, 512, 220, 1, 510, 241...</td>\n",
       "      <td>(421, 750)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>{'human3': [383, 92, 701, 1005], 'human2': [10...</td>\n",
       "      <td>bb0fc3527876071ff1732cfdddf13492b92fd2e1</td>\n",
       "      <td>{'human1': [197, 177, 1, 199, 305, 1, 314, 366...</td>\n",
       "      <td>(634, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>{'human1': [0, 2, 762, 1126]}</td>\n",
       "      <td>bb1733674134cad32e802318891ac80f098a8d3f</td>\n",
       "      <td>{'human1': [341, 295, 1, 441, 314, 1, 549, 259...</td>\n",
       "      <td>(1000, 750)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>{'human5': [0, 364, 1000, 671], 'human4': [253...</td>\n",
       "      <td>bc7b7a0819a0b64727a5cfdc84eabb665516e93e</td>\n",
       "      <td>{'human1': [216, 210, 1, 211, 345, 1, 285, 414...</td>\n",
       "      <td>(818, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>{'human1': [299, 75, 740, 1000]}</td>\n",
       "      <td>ad366355eafe1cf943c2e41e2e0a8d23135fd7d9</td>\n",
       "      <td>{'human2': [457, 617, 1, 466, 556, 1, 444, 515...</td>\n",
       "      <td>(900, 600)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>{'human2': [118, 279, 900, 683], 'human1': [22...</td>\n",
       "      <td>282b302a0d5d133e212f03f9ada610b12859941b</td>\n",
       "      <td>{'human1': [215, 271, 1, 145, 380, 1, 221, 398...</td>\n",
       "      <td>(959, 603)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>{'human4': [425, 0, 804, 530], 'human3': [409,...</td>\n",
       "      <td>0ade7caa8014689f193e7bbbe47711ad413f4dc2</td>\n",
       "      <td>{'human3': [215, 216, 1, 187, 250, 1, 147, 289...</td>\n",
       "      <td>(677, 900)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>{'human2': [126, 5, 533, 800], 'human1': [7, 7...</td>\n",
       "      <td>e6adb9d437268678c3daa1487c113cfd2c1f383f</td>\n",
       "      <td>{'human2': [849, 224, 1, 873, 293, 1, 885, 366...</td>\n",
       "      <td>(665, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>{'human3': [0, 253, 599, 983], 'human2': [77, ...</td>\n",
       "      <td>c38410f42ea787dc34c66cc459889707ff426803</td>\n",
       "      <td>{'human1': [260, 389, 1, 316, 629, 1, 504, 599...</td>\n",
       "      <td>(644, 930)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>{'human2': [0, 0, 650, 1115], 'human1': [28, 0...</td>\n",
       "      <td>e3949a4a32ecb7f3089b9a807d2b6620edba91d4</td>\n",
       "      <td>{'human1': [336, 298, 1, 324, 425, 1, 342, 538...</td>\n",
       "      <td>(666, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'human1': [39, 0, 593, 946]}</td>\n",
       "      <td>094ade15553543ed44a2020d5a19db22062d98a8</td>\n",
       "      <td>{'human8': [513, 203, 2, 525, 244, 2, 512, 279...</td>\n",
       "      <td>(454, 615)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>{'human2': [454, 68, 940, 602], 'human1': [233...</td>\n",
       "      <td>b978c1ccc143e004a11a5d5438a5322f40dca554</td>\n",
       "      <td>{'human1': [236, 167, 2, 274, 149, 1, 248, 211...</td>\n",
       "      <td>(800, 591)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>{'human1': [127, 4, 597, 1004]}</td>\n",
       "      <td>5408b0fe4dd0383e622136fe51b352fc808ced0f</td>\n",
       "      <td>{'human1': [231, 204, 1, 229, 347, 2, 197, 447...</td>\n",
       "      <td>(843, 564)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>{'human2': [437, 75, 687, 1101], 'human1': [2,...</td>\n",
       "      <td>75493c9c4d8d3130590dd234e212349747f74ba8</td>\n",
       "      <td>{'human2': [583, 384, 1, 557, 480, 1, 549, 439...</td>\n",
       "      <td>(809, 940)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>{'human1': [162, 0, 550, 885]}</td>\n",
       "      <td>357bda3796c5372151c993e2456fa50712012ea8</td>\n",
       "      <td>{'human2': [216, 222, 1, 222, 293, 2, 202, 283...</td>\n",
       "      <td>(600, 400)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>{'human2': [379, 0, 900, 686], 'human1': [237,...</td>\n",
       "      <td>011e32d88ddd78cf844719c22bee78f0c4d9fd4e</td>\n",
       "      <td>{'human3': [754, 220, 2, 772, 318, 2, 758, 391...</td>\n",
       "      <td>(900, 875)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>{'human1': [17, 0, 1037, 819]}</td>\n",
       "      <td>768696b4fe1ecc31e5ec8de30acdf6bb94c2f282</td>\n",
       "      <td>{'human1': [212, 222, 2, 189, 319, 2, 168, 412...</td>\n",
       "      <td>(811, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>{'human2': [0, 0, 900, 693], 'human1': [497, 2...</td>\n",
       "      <td>fd9b76dee6f1d003eda67592b1bfb5fa256d4f81</td>\n",
       "      <td>{'human1': [245, 220, 1, 212, 346, 1, 230, 425...</td>\n",
       "      <td>(687, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>{'human2': [115, 24, 605, 790], 'human1': [0, ...</td>\n",
       "      <td>864d09b2f91c4d802af30a7c3f08304d5b09a98b</td>\n",
       "      <td>{'human2': [264, 238, 2, 278, 311, 2, 292, 277...</td>\n",
       "      <td>(825, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>{'human2': [50, 0, 657, 945], 'human1': [277, ...</td>\n",
       "      <td>7282ff93c7ffccc31561de917c47f4b1b3593ef8</td>\n",
       "      <td>{'human1': [388, 260, 1, 464, 334, 1, 557, 286...</td>\n",
       "      <td>(939, 741)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>{'human4': [0, 0, 650, 554], 'human3': [141, 3...</td>\n",
       "      <td>781827a1c05024b4520b14f880d89e1c764c32ca</td>\n",
       "      <td>{'human2': [319, 271, 1, 264, 260, 1, 202, 216...</td>\n",
       "      <td>(707, 948)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>{'human1': [105, 0, 600, 1016]}</td>\n",
       "      <td>6c893f6f5eb02c8abf2fe696334373564fe4d988</td>\n",
       "      <td>{'human1': [80, 326, 1, 102, 484, 1, 114, 646,...</td>\n",
       "      <td>(800, 694)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>{'human1': [41, 0, 520, 890]}</td>\n",
       "      <td>6f3c83aa9fc82f12f6dc0dd8bfc4092df34a4dca</td>\n",
       "      <td>{'human2': [539, 282, 1, 504, 368, 1, 465, 450...</td>\n",
       "      <td>(948, 793)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>{'human1': [192, 220, 666, 1000]}</td>\n",
       "      <td>3dd8d04858029eecebbbcf1d0297bece1161b174</td>\n",
       "      <td>{'human1': [167, 286, 1, 231, 412, 1, 349, 478...</td>\n",
       "      <td>(1000, 666)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>{'human1': [137, 0, 666, 1141]}</td>\n",
       "      <td>276f3b6e41f3f4a973bb8a4482a41fe97ab386db</td>\n",
       "      <td>{'human1': [510, 241, 1, 492, 345, 1, 562, 340...</td>\n",
       "      <td>(606, 900)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>{'human2': [363, 54, 700, 466], 'human1': [204...</td>\n",
       "      <td>818cf5d67ed235a0b206703cf143c72fea87525a</td>\n",
       "      <td>{'human1': [212, 172, 2, 192, 298, 1, 221, 206...</td>\n",
       "      <td>(825, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>{'human2': [205, 0, 658, 1135], 'human1': [0, ...</td>\n",
       "      <td>c5aa021fab5092a0465744f107020503f6e0878c</td>\n",
       "      <td>{'human2': [360, 176, 1, 352, 272, 1, 396, 309...</td>\n",
       "      <td>(657, 940)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>{'human3': [0, 12, 900, 809], 'human2': [429, ...</td>\n",
       "      <td>477ac3ce26bdcd029b37aba8c00dca0b359a6d8d</td>\n",
       "      <td>{'human2': [320, 297, 1, 271, 373, 2, 235, 422...</td>\n",
       "      <td>(1000, 742)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>{'human2': [125, 0, 533, 874], 'human1': [36, ...</td>\n",
       "      <td>404596910e5dc7fc1ea37f5a05fb28c387580002</td>\n",
       "      <td>{'human3': [35, 265, 1, 0, 0, 3, 25, 329, 1, 1...</td>\n",
       "      <td>(666, 1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>{'human2': [379, 0, 900, 601], 'human1': [240,...</td>\n",
       "      <td>30ecff9a3207c3b885534d347f2bb00add0dc8a9</td>\n",
       "      <td>{'human3': [342, 223, 1, 346, 360, 1, 256, 405...</td>\n",
       "      <td>(805, 550)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>{'human2': [84, 49, 600, 844], 'human1': [220,...</td>\n",
       "      <td>3a1376d13a11f00e2f0023e911af6a27faaff2be</td>\n",
       "      <td>{'human1': [141, 307, 1, 97, 406, 1, 127, 441,...</td>\n",
       "      <td>(633, 950)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>{'human3': [574, 0, 950, 726], 'human2': [289,...</td>\n",
       "      <td>9f2f8a6038be76040fbeaca4e8c64fb1f61d02f6</td>\n",
       "      <td>{'human2': [645, 183, 1, 581, 325, 1, 481, 284...</td>\n",
       "      <td>(532, 800)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     human_annotations  \\\n",
       "0    {'human2': [344, 162, 749, 1000], 'human1': [9...   \n",
       "1    {'human3': [525, 0, 1000, 690], 'human2': [335...   \n",
       "10   {'human3': [148, 0, 940, 711], 'human2': [0, 0...   \n",
       "100  {'human4': [367, 0, 570, 853], 'human3': [426,...   \n",
       "101                     {'human1': [193, 0, 939, 705]}   \n",
       "102                    {'human1': [56, 18, 743, 1000]}   \n",
       "103  {'human3': [396, 82, 666, 1000], 'human2': [30...   \n",
       "104  {'human2': [0, 91, 641, 710], 'human1': [0, 15...   \n",
       "105  {'human8': [448, 86, 615, 460], 'human7': [513...   \n",
       "106                       {'human1': [0, 0, 784, 894]}   \n",
       "107  {'human2': [245, 23, 530, 398], 'human1': [306...   \n",
       "108  {'human2': [203, 0, 1000, 765], 'human1': [0, ...   \n",
       "109                     {'human1': [72, 0, 640, 1027]}   \n",
       "11   {'human2': [419, 1, 950, 633], 'human1': [18, ...   \n",
       "110                     {'human1': [102, 0, 564, 992]}   \n",
       "111                      {'human1': [0, 0, 630, 1030]}   \n",
       "112  {'human3': [101, 0, 950, 633], 'human2': [127,...   \n",
       "113  {'human2': [316, 0, 950, 726], 'human1': [26, ...   \n",
       "114                      {'human1': [19, 0, 558, 829]}   \n",
       "115  {'human2': [67, 0, 550, 825], 'human1': [83, 0...   \n",
       "116                     {'human1': [286, 0, 940, 703]}   \n",
       "117                    {'human1': [290, 0, 1000, 848]}   \n",
       "118                     {'human1': [104, 0, 600, 997]}   \n",
       "119  {'human3': [0, 118, 799, 1000], 'human2': [0, ...   \n",
       "12   {'human3': [0, 83, 940, 720], 'human2': [83, 0...   \n",
       "120  {'human2': [148, 180, 657, 879], 'human1': [17...   \n",
       "121  {'human2': [66, 0, 950, 814], 'human1': [27, 0...   \n",
       "122                      {'human1': [20, 0, 535, 700]}   \n",
       "123                      {'human1': [5, 0, 626, 1116]}   \n",
       "124  {'human3': [383, 92, 701, 1005], 'human2': [10...   \n",
       "..                                                 ...   \n",
       "72                       {'human1': [0, 2, 762, 1126]}   \n",
       "73   {'human5': [0, 364, 1000, 671], 'human4': [253...   \n",
       "74                    {'human1': [299, 75, 740, 1000]}   \n",
       "75   {'human2': [118, 279, 900, 683], 'human1': [22...   \n",
       "76   {'human4': [425, 0, 804, 530], 'human3': [409,...   \n",
       "77   {'human2': [126, 5, 533, 800], 'human1': [7, 7...   \n",
       "78   {'human3': [0, 253, 599, 983], 'human2': [77, ...   \n",
       "79   {'human2': [0, 0, 650, 1115], 'human1': [28, 0...   \n",
       "8                        {'human1': [39, 0, 593, 946]}   \n",
       "80   {'human2': [454, 68, 940, 602], 'human1': [233...   \n",
       "81                     {'human1': [127, 4, 597, 1004]}   \n",
       "82   {'human2': [437, 75, 687, 1101], 'human1': [2,...   \n",
       "83                      {'human1': [162, 0, 550, 885]}   \n",
       "84   {'human2': [379, 0, 900, 686], 'human1': [237,...   \n",
       "85                      {'human1': [17, 0, 1037, 819]}   \n",
       "86   {'human2': [0, 0, 900, 693], 'human1': [497, 2...   \n",
       "87   {'human2': [115, 24, 605, 790], 'human1': [0, ...   \n",
       "88   {'human2': [50, 0, 657, 945], 'human1': [277, ...   \n",
       "89   {'human4': [0, 0, 650, 554], 'human3': [141, 3...   \n",
       "9                      {'human1': [105, 0, 600, 1016]}   \n",
       "90                       {'human1': [41, 0, 520, 890]}   \n",
       "91                   {'human1': [192, 220, 666, 1000]}   \n",
       "92                     {'human1': [137, 0, 666, 1141]}   \n",
       "93   {'human2': [363, 54, 700, 466], 'human1': [204...   \n",
       "94   {'human2': [205, 0, 658, 1135], 'human1': [0, ...   \n",
       "95   {'human3': [0, 12, 900, 809], 'human2': [429, ...   \n",
       "96   {'human2': [125, 0, 533, 874], 'human1': [36, ...   \n",
       "97   {'human2': [379, 0, 900, 601], 'human1': [240,...   \n",
       "98   {'human2': [84, 49, 600, 844], 'human1': [220,...   \n",
       "99   {'human3': [574, 0, 950, 726], 'human2': [289,...   \n",
       "\n",
       "                                     image_id  \\\n",
       "0    5f9ab0e1b59cb27de8adf0255a3730536ed87b07   \n",
       "1    8f2b73eda5bc107b58761be170ea669732af57f2   \n",
       "10   44ccfe343f594896c82b1e50438219744ccbab88   \n",
       "100  e2ff8b814590cf92777cb20bf4c788e54754a6fa   \n",
       "101  edad47c66307b56bd24c87982bcb408df0ad91af   \n",
       "102  09a244a6ee20766f226c0f030ffef7a89a9c2f17   \n",
       "103  3af84f31379fcce70dacb65aca44f298b624ab2c   \n",
       "104  62151d25e5caa2bccb1b571a5a6cf371369028b4   \n",
       "105  7aeec8e64afd04c23c4988a647a851a3e7d52ccb   \n",
       "106  2bd9db0a2599ba6ca8c3351c471a29e24c36af3e   \n",
       "107  d78fe2735a101dd45d8e931a80bd8f6a460dd587   \n",
       "108  7df555633d2e860814a047dbc5d3a5bc0b22fa25   \n",
       "109  9a7f971d79a4e00158e20b1f462236bb4dfba344   \n",
       "11   040dc1b0e086f0415adb6e8389e3c20b505c38c0   \n",
       "110  522bbcea7b6243c83ae467397467a64853c5764a   \n",
       "111  a1f254a8dc1cbfbede5aab5b49a3b677f29e77a6   \n",
       "112  0bbc67ad0ee0c94f553ce7757a67b743c79a80b1   \n",
       "113  8b8c7e42e2c03b6e79a384d9dfab84dbf772d191   \n",
       "114  9920d24cb34e1c2947bf446853d9f5a5354ed0bf   \n",
       "115  96d42665d10bc6bbe4908a891b4e553eb9676661   \n",
       "116  a0a57eb5135c7db48688a500708265e7636908ef   \n",
       "117  4f8f57734666ce759f0baa3b6f6068d0b0699b53   \n",
       "118  7e5abdf7dd47a8de196014c9bd4bb7b904ddd444   \n",
       "119  144182728d1d2edde9dcf8f5324b6c76998add2d   \n",
       "12   9352648e31d3f7afc6dea79bb28506808e348465   \n",
       "120  4cf280565329eb8dc721c7d2521505596e2d8089   \n",
       "121  45e3848616c5ba3db4eca73f81c6f5c88e1789c6   \n",
       "122  7e0ab7c43a0deb97168825aafd5071485731407c   \n",
       "123  1d6d88db2d2930942eb83f1818129942dafdf6d8   \n",
       "124  bb0fc3527876071ff1732cfdddf13492b92fd2e1   \n",
       "..                                        ...   \n",
       "72   bb1733674134cad32e802318891ac80f098a8d3f   \n",
       "73   bc7b7a0819a0b64727a5cfdc84eabb665516e93e   \n",
       "74   ad366355eafe1cf943c2e41e2e0a8d23135fd7d9   \n",
       "75   282b302a0d5d133e212f03f9ada610b12859941b   \n",
       "76   0ade7caa8014689f193e7bbbe47711ad413f4dc2   \n",
       "77   e6adb9d437268678c3daa1487c113cfd2c1f383f   \n",
       "78   c38410f42ea787dc34c66cc459889707ff426803   \n",
       "79   e3949a4a32ecb7f3089b9a807d2b6620edba91d4   \n",
       "8    094ade15553543ed44a2020d5a19db22062d98a8   \n",
       "80   b978c1ccc143e004a11a5d5438a5322f40dca554   \n",
       "81   5408b0fe4dd0383e622136fe51b352fc808ced0f   \n",
       "82   75493c9c4d8d3130590dd234e212349747f74ba8   \n",
       "83   357bda3796c5372151c993e2456fa50712012ea8   \n",
       "84   011e32d88ddd78cf844719c22bee78f0c4d9fd4e   \n",
       "85   768696b4fe1ecc31e5ec8de30acdf6bb94c2f282   \n",
       "86   fd9b76dee6f1d003eda67592b1bfb5fa256d4f81   \n",
       "87   864d09b2f91c4d802af30a7c3f08304d5b09a98b   \n",
       "88   7282ff93c7ffccc31561de917c47f4b1b3593ef8   \n",
       "89   781827a1c05024b4520b14f880d89e1c764c32ca   \n",
       "9    6c893f6f5eb02c8abf2fe696334373564fe4d988   \n",
       "90   6f3c83aa9fc82f12f6dc0dd8bfc4092df34a4dca   \n",
       "91   3dd8d04858029eecebbbcf1d0297bece1161b174   \n",
       "92   276f3b6e41f3f4a973bb8a4482a41fe97ab386db   \n",
       "93   818cf5d67ed235a0b206703cf143c72fea87525a   \n",
       "94   c5aa021fab5092a0465744f107020503f6e0878c   \n",
       "95   477ac3ce26bdcd029b37aba8c00dca0b359a6d8d   \n",
       "96   404596910e5dc7fc1ea37f5a05fb28c387580002   \n",
       "97   30ecff9a3207c3b885534d347f2bb00add0dc8a9   \n",
       "98   3a1376d13a11f00e2f0023e911af6a27faaff2be   \n",
       "99   9f2f8a6038be76040fbeaca4e8c64fb1f61d02f6   \n",
       "\n",
       "                                  keypoint_annotations image_height_width  \n",
       "0    {'human2': [430, 370, 1, 400, 444, 1, 474, 451...        (1000, 749)  \n",
       "1    {'human3': [722, 155, 1, 682, 178, 1, 622, 189...        (690, 1000)  \n",
       "10   {'human2': [354, 115, 1, 366, 148, 1, 374, 176...         (356, 530)  \n",
       "100  {'human1': [257, 303, 1, 201, 402, 1, 230, 503...         (939, 633)  \n",
       "101  {'human2': [616, 183, 2, 602, 291, 2, 561, 222...        (667, 1000)  \n",
       "102  {'human4': [568, 295, 1, 556, 316, 2, 537, 293...         (517, 690)  \n",
       "103  {'human2': [275, 191, 1, 256, 200, 1, 235, 206...         (413, 550)  \n",
       "104  {'human1': [683, 249, 1, 676, 175, 1, 731, 140...        (666, 1000)  \n",
       "105  {'human3': [670, 290, 1, 683, 395, 1, 803, 381...         (626, 940)  \n",
       "106  {'human2': [263, 310, 1, 266, 435, 1, 317, 523...         (750, 500)  \n",
       "107  {'human1': [140, 316, 1, 142, 491, 1, 138, 631...         (735, 550)  \n",
       "108  {'human2': [27, 169, 1, 0, 0, 3, 0, 0, 3, 166,...        (1000, 666)  \n",
       "109  {'human1': [416, 202, 1, 354, 280, 1, 287, 336...        (665, 1000)  \n",
       "11   {'human2': [437, 373, 1, 410, 510, 1, 417, 629...        (666, 1000)  \n",
       "110  {'human6': [860, 369, 1, 845, 404, 2, 838, 434...        (666, 1000)  \n",
       "111  {'human3': [259, 143, 2, 170, 171, 1, 116, 224...         (693, 950)  \n",
       "112  {'human1': [244, 268, 1, 165, 342, 1, 223, 385...         (940, 668)  \n",
       "113  {'human4': [0, 0, 3, 0, 0, 3, 0, 0, 3, 0, 0, 3...         (646, 950)  \n",
       "114  {'human1': [283, 374, 1, 325, 460, 1, 357, 540...        (1000, 711)  \n",
       "115  {'human2': [174, 276, 1, 155, 394, 1, 129, 484...         (941, 600)  \n",
       "116  {'human3': [767, 234, 1, 717, 301, 1, 678, 380...         (795, 950)  \n",
       "117  {'human2': [133, 195, 2, 128, 259, 2, 119, 296...         (751, 500)  \n",
       "118  {'human2': [351, 257, 1, 362, 376, 1, 415, 475...         (939, 626)  \n",
       "119  {'human1': [335, 308, 2, 296, 352, 1, 201, 343...        (1000, 665)  \n",
       "12   {'human1': [158, 202, 1, 166, 336, 1, 289, 324...         (960, 640)  \n",
       "120  {'human2': [496, 529, 1, 435, 541, 1, 392, 500...        (1000, 769)  \n",
       "121  {'human2': [637, 271, 1, 651, 388, 1, 649, 444...        (1000, 667)  \n",
       "122  {'human2': [392, 176, 1, 384, 208, 2, 378, 220...         (718, 691)  \n",
       "123  {'human2': [516, 187, 1, 512, 220, 1, 510, 241...         (421, 750)  \n",
       "124  {'human1': [197, 177, 1, 199, 305, 1, 314, 366...         (634, 950)  \n",
       "..                                                 ...                ...  \n",
       "72   {'human1': [341, 295, 1, 441, 314, 1, 549, 259...        (1000, 750)  \n",
       "73   {'human1': [216, 210, 1, 211, 345, 1, 285, 414...         (818, 550)  \n",
       "74   {'human2': [457, 617, 1, 466, 556, 1, 444, 515...         (900, 600)  \n",
       "75   {'human1': [215, 271, 1, 145, 380, 1, 221, 398...         (959, 603)  \n",
       "76   {'human3': [215, 216, 1, 187, 250, 1, 147, 289...         (677, 900)  \n",
       "77   {'human2': [849, 224, 1, 873, 293, 1, 885, 366...        (665, 1000)  \n",
       "78   {'human1': [260, 389, 1, 316, 629, 1, 504, 599...         (644, 930)  \n",
       "79   {'human1': [336, 298, 1, 324, 425, 1, 342, 538...        (666, 1000)  \n",
       "8    {'human8': [513, 203, 2, 525, 244, 2, 512, 279...         (454, 615)  \n",
       "80   {'human1': [236, 167, 2, 274, 149, 1, 248, 211...         (800, 591)  \n",
       "81   {'human1': [231, 204, 1, 229, 347, 2, 197, 447...         (843, 564)  \n",
       "82   {'human2': [583, 384, 1, 557, 480, 1, 549, 439...         (809, 940)  \n",
       "83   {'human2': [216, 222, 1, 222, 293, 2, 202, 283...         (600, 400)  \n",
       "84   {'human3': [754, 220, 2, 772, 318, 2, 758, 391...         (900, 875)  \n",
       "85   {'human1': [212, 222, 2, 189, 319, 2, 168, 412...         (811, 550)  \n",
       "86   {'human1': [245, 220, 1, 212, 346, 1, 230, 425...         (687, 950)  \n",
       "87   {'human2': [264, 238, 2, 278, 311, 2, 292, 277...         (825, 550)  \n",
       "88   {'human1': [388, 260, 1, 464, 334, 1, 557, 286...         (939, 741)  \n",
       "89   {'human2': [319, 271, 1, 264, 260, 1, 202, 216...         (707, 948)  \n",
       "9    {'human1': [80, 326, 1, 102, 484, 1, 114, 646,...         (800, 694)  \n",
       "90   {'human2': [539, 282, 1, 504, 368, 1, 465, 450...         (948, 793)  \n",
       "91   {'human1': [167, 286, 1, 231, 412, 1, 349, 478...        (1000, 666)  \n",
       "92   {'human1': [510, 241, 1, 492, 345, 1, 562, 340...         (606, 900)  \n",
       "93   {'human1': [212, 172, 2, 192, 298, 1, 221, 206...         (825, 550)  \n",
       "94   {'human2': [360, 176, 1, 352, 272, 1, 396, 309...         (657, 940)  \n",
       "95   {'human2': [320, 297, 1, 271, 373, 2, 235, 422...        (1000, 742)  \n",
       "96   {'human3': [35, 265, 1, 0, 0, 3, 25, 329, 1, 1...        (666, 1000)  \n",
       "97   {'human3': [342, 223, 1, 346, 360, 1, 256, 405...         (805, 550)  \n",
       "98   {'human1': [141, 307, 1, 97, 406, 1, 127, 441,...         (633, 950)  \n",
       "99   {'human2': [645, 183, 1, 581, 325, 1, 481, 284...         (532, 800)  \n",
       "\n",
       "[210 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "b3e4d858-02de-4ae0-9622-2e88f7adcf77"
    }
   },
   "outputs": [],
   "source": [
    "new_coords = np.array(new_coords)\n",
    "hw = np.array(hw)\n",
    "\n",
    "for c in range(2):\n",
    "    np.clip(new_coords[:, c+2], a_min=None, a_max=hw[:, c], out=new_coords[:, c+2])\n",
    "\n",
    "new_coords[np.where(new_coords<0)] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "f2762411-12b1-462f-b860-a95748ec8b3e"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import skimage.io as io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "98ea617d-1a77-427d-8b24-85a021cde0e8"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "nbpresent": {
     "id": "eac2958a-acd9-446d-8263-6752af471c5d"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Rectangle at 0x7f57c5535908>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0JYd9aFswzuP2zohLR9c9z8lRy+1bN7h9+xbL5Zzj42PKosBiMWbdR2KxWHDn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TzAHXd+jt/94z/ju9/x/ey9+Dq+dPcXmLMVDyyfpD09Tyk167rANUNmhCDY3uGRJ07wPd95HWml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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f57c7385748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "currentAxis = plt.gca()\n",
    "coord = [ 344.3, 162.25, 601.7, 818.75]\n",
    "c = np.array((coord[0], coord[1]))\n",
    "# print(c)\n",
    "h = coord[3]-coord[1]\n",
    "w = coord[2]-coord[0]\n",
    "coords = c, w, h\n",
    "img = io.imread('/media/bnrc2/_backup/ai/ai_challenger_keypoint_train_20170902/keypoint_train_images_20170902/5f9ab0e1b59cb27de8adf0255a3730536ed87b07.jpg')\n",
    "plt.imshow(img)\n",
    "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='blue', linewidth=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "efe32f3c-125e-4c36-b165-c0823a6f752a"
    }
   },
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "nbpresent": {
     "id": "cfd7e0b7-621b-4bb8-9a92-b6db4d41a603"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/bnrc2/anaconda3/bin/python3'"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.executable"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  },
  "nbpresent": {
   "slides": {
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